Affiliations: [a] Faculté des Sciences de Tunis, Ecole Nationale d'Ingénieurs de Tunis, Université de Tunis El Manar, Tunis, Tunis | [b] Ecole Nationale d'Ingénieurs de Tunis, Université de Tunis El Manar, Tunis, Tunis
Correspondence:
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Corresponding author: Amel Grissa Touzi, Faculté des Sciences de Tunis, Ecole Nationale d'Ingénieurs de Tunis, Université de Tunis El Manar, Tunis, Tunis. E-mail: amel.touzi@enit.rnu.tn
Abstract: While Knowledge Discovery in Databases (KDD) have enjoyed great popularity and success in the recent years, these approaches are restricted to the application of discovery and modeling techniques within the KDD process. Thus, the goal to exploit these data is often neglected. In this paper, we propose an intelligent approach for exploitation of these data. For this, we propose to define an Expert System (ES) allowing the user to easily exploit the large data set. The Knowledge Base (KB) of our ES is defined by introducing a new KDD approach taking in consideration another degree of granularity into the process of knowledge extraction. This set represents a reduced knowledge of the initial data set and allows deducting the semantics of the data. We prove that, this ES can help the user to give semantics for these data and to exploit them in intelligent way.
Keywords: Knowledge discovery databases, expert system, large data sets
